IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v40y2021i3p367-386.html

Forecasting negative yield‐curve distributions

Author

Listed:
  • Jae‐Yun Jun
  • Victor Lebreton
  • Yves Rakotondratsimba

Abstract

Negative interest rates have been present in various marketplaces since mid‐2014, following the negative interest rate policy (NIRP) adopted by the European Central Bank to raise economic growth. The well‐known historical approach (HA) appears to be a good resource. By tweaking the HA, we derive a very tractable data‐driven tool that allows practitioners to generate yield‐curve distributions at future discrete time horizons. We thereby provide a robust and easy‐to‐understand forecasting model, suitable for the NIRP context, allowing an appreciation of its predictive power. Besides the methodological development herein, various numerical illustrations are presented to shed light on the benefits (and limitations) of this forecasting approach.

Suggested Citation

  • Jae‐Yun Jun & Victor Lebreton & Yves Rakotondratsimba, 2021. "Forecasting negative yield‐curve distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 367-386, April.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:367-386
    DOI: 10.1002/for.2727
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2727
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2727?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2017. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1257-1275, August.
    2. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
    3. Pedro Gurrola-Perez & David Murphy, 2015. "Filtered historical simulation Value-at-Risk models and their competitors," Bank of England working papers 525, Bank of England.
    4. Claudio Borio & Leonardo Gambacorta & Boris Hofmann, 2017. "The influence of monetary policy on bank profitability," International Finance, Wiley Blackwell, vol. 20(1), pages 48-63, March.
    5. Lemke, Wolfgang & Vladu, Andreea L., 2016. "Below the zero lower bound: A shadow-rate term structure model for the euro area," Discussion Papers 32/2016, Deutsche Bundesbank.
    6. Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Kortela, Tomi, 2016. "A shadow rate model with time-varying lower bound of interest rates," Research Discussion Papers 19/2016, Bank of Finland.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Michael D. Bauer & Glenn D. Rudebusch, 2016. "Monetary Policy Expectations at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(7), pages 1439-1465, October.
    11. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    13. Andreas Jobst & Ms. Huidan Huidan Lin, 2016. "Negative Interest Rate Policy (NIRP): Implications for Monetary Transmission and Bank Profitability in the Euro Area," IMF Working Papers 2016/172, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Cynthia Wu & Fan Dora Xia, 2020. "Negative interest rate policy and the yield curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 653-672, September.
    2. Junttila, Juha & Nguyen, Vo Cao Sang, 2022. "Impacts of sovereign risk premium on bank profitability: Evidence from euro area," International Review of Financial Analysis, Elsevier, vol. 81(C).
    3. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    4. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    5. Kuusela, Annika & Hännikäinen, Jari, 2017. "What do the shadow rates tell us about future inflation?," MPRA Paper 80542, University Library of Munich, Germany.
    6. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    7. De Rezende, Rafael B. & Ristiniemi, Annukka, 2023. "A shadow rate without a lower bound constraint," Journal of Banking & Finance, Elsevier, vol. 146(C).
    8. Salisu, Afees A. & Isah, Kazeem O. & Cepni, Oguzhan, 2024. "Conventional and unconventional shadow rates and the US state-level stock returns: Evidence from non-stationary heterogeneous panels," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    9. Jose David GARCIA REVELO & Yannick LUCOTTE & Florian PRADINES-JOBET, 2019. "Macroprudential and Monetary Policies : The Need to Dance the Tango in Harmony," LEO Working Papers / DR LEO 2691, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    10. Claus, Edda & Claus, Iris & Krippner, Leo, 2018. "Asset market responses to conventional and unconventional monetary policy shocks in the United States," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 270-282.
    11. Barbara Rossi, 2018. "Identifying and estimating the effects of unconventional monetary policy in the data: How to do It and what have we learned?," Economics Working Papers 1641, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2020.
    12. Barbara Rossi, 2021. "Identifying and estimating the effects of unconventional monetary policy: How to do it and what have we learned?," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 1-32.
    13. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    14. Mohammadi, M. & Rezakhah, S. & Modarresi, N., 2020. "Semi-Lévy driven continuous-time GARCH process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    15. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    16. Jean-Guillaume Sahuc & Grégory Levieuge, 2020. "Monetary Policy Transmission with Downward Interest Rate Rigidity," Working Papers hal-04159706, HAL.
    17. Horvath, Roman & Kotlebova, Jana & Siranova, Maria, 2018. "Interest rate pass-through in the euro area: Financial fragmentation, balance sheet policies and negative rates," Journal of Financial Stability, Elsevier, vol. 36(C), pages 12-21.
    18. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    19. Bloznelis, Daumantas, 2017. "Hedging under square loss," MPRA Paper 83442, University Library of Munich, Germany.
    20. Coroneo, Laura & Pastorello, Sergio, 2020. "European spreads at the interest rate lower bound," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:367-386. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.